The advent of information technology during the last decade has had a major impact on today's civilization. In the industrial process control world, the information revolution has brought about major changes. Intelligence, such as control algorithms existing in the current instrument layer, is moving up to the supervisory computer layer or moving down to the sensor/transmitter layer. It is the fieldbus, a digital communication networks for sensor, device, and field, that leads to this change. The benefits of using fieldbus technology may include, i.a., wiring savings, more flexible and powerful control implementation options, two-way maintenance and diagnostic information Thus, future process control systems will be implemented by fieldbus controllers and computers with a field bus connection. The conventional instrument layer including Distributed Control Systems (DCS) and Programmable Logic Controllers (PLC) will eventually disappear.
The fieldbus controller, as the name suggests, is a controller connected to the fieldbus and may be packaged inside a transmitter enclosure. Since, the fieldbus controller is installed in the field, not in the control room, it should be very robust and work continuously without attention. This kind of controller requires solid hardware, software, and control algorithm. Since the current conventional proportional-integral-derivative (PID) control algorithm requires manual tuning, it is not always a good solution for a fieldbus controller.
In the past few years, the quality, functionality, and reliability of personal computers (PCs) have improved substantially. With Microsoft's multitasking Windows NT operating system, a PC can be a reliable and feasible device for mission- critical applications such as controlling process loops directly.
Facing this major change, the traditional process control world is ill prepared. Decades-old control schemes such as PID are still commonly in use. On the factory floor, we frequently face complex control problems that require high level expertise to resolve. At the same time, ill-prepared operators typically run the processes day and night. This is a fact that is overlooked and cannot be discounted. It is thus desirable to provide control technology and products to ordinary operators that will allow them to easily and effectively control simple to complex processes.
The existing control technology in process control area is basically is follows:
1. PID Control
The most widely used industrial controller today is still the old PID controller. PID is simple, easy to implement, and requires no process model, but has major shortcomings. Firstly, PID works for the process that is basically linear, time-invariant, and may have only small or no dynamic changes. These conditions are too restrictive for many industrial processes. Secondly, PID has to be tuned right by the user; that is, its parameters have to be set properly based on the process dynamics. In real applications, tuning of a PID is often a frustrating experience. And last, PID cannot work effectively in controlling complex systems which are usually nonlinear, time-variant, coupled, and have parameter or structure uncertainties. On the factory floor, it is very common to see that many loops are left in the manual mode because the operators have trouble keeping the control loop running smoothly in the closed-loop automatic mode. Due to these shortcomings, many industrial control systems today suffer safety, quality, energy waste, and productivity problems by continuing to use PID control.
Some PID self-tuning methods have been developed to deal with PID tuning problems. Many commercial single loop controllers and distributed control systems are equipped with auto-tuning or self-tuning PID controllers. But their applications have met major obstacles. If the self-tuning is model based, it requires insertion of a bump in the closed-loop situation in order to find the process model on-line to re-tune the PID. Operators find this procedure uncomfortable. If the self-tuning is rule based, it is often difficult to distinguish between the effects of load disturbances and genuine changes in the process dynamics. The controller may thus overreact to a disturbance and create an unnecessary adaptation transition. In addition, in a rule based system, the reliability of the tuning may be questionable since there are no mature stability analysis methods available for the rule based systems. Therefore, experience has shown that many self-tuning PID controllers are being operated in the so called auto-tuning mode rather than in the continuous self-tuning mode. Auto-tuning is usually defined as a feature in which the PID parameters are calculated automatically based on a simplified process model that may be acquired in the open-loop situation.
2. Adaptive Control
An adaptive control system can be defined as a feedback control system intelligent enough to adjust its characteristics in a changing environment so as to operate in an optimal manner according to some specified criteria. In general, adaptive control systems have achieved great success in aircraft, missile, and spacecraft control applications. In industrial process control applications, however, the traditional adaptive control has not been very successful. The most credible achievement is just the above-described PID self-tuning scheme that is widely implemented in commercial products but not very well used or accepted by the user.
Traditional adaptive control methods, either model reference or self-tuning, usually require some kind of identification for the process dynamics. This Contributes to a number of fundamental problems such as the amount of off line training it may require, the tradeoff between the persistent excitation of signals for correct identification and the steady system response for control performance, the assumption of the process structure, the model convergence and system stability issues in real applications. In addition, traditional adaptive control methods assume the knowledge of the process structure. They have major difficulties in dealing with nonlinear, structure variant, or large time delayed processes
3. Robust Control
Robust control is a controller design method that focuses on the reliability (robustness) of the control law. Robustness is usually defined as the minimum requirement a control system has to satisfy to be useful in a practical environment. Once the controller is designed, its parameters do not change and control performances are guaranteed. The robust control methods, either in time domain or frequency domain, usually assume the knowledge of process dynamics and its variation ranges. Some algorithms may not need a precise process model but then require some kind of off-line identification. The design of a robust control system is typically based on the worst case scenario, so that the system usually does not work at optimal status in sense of control performance under normal circumstances.
Robust control methods are well suited in applications where the control system stability and reliability are the top priorities, process dynamics are known, and variation ranges for uncertainties can be estimated. Aircraft and spacecraft controls are some examples of these systems. In process control applications, some control systems can also be designed with robust control methods. However, the design of a robust control system requires high level expertise. Once the design is done, the system works well. But on the other hand, the system has to be redesigned when upgrades or major modifications are required.
4. Predictive Control
Predictive control is probably the only advanced control method used successfully in industrial control applications so far. The essence of predictive control is based on three key elements: (1) predictive model, (2) optimization in range of a temporal window, and (3) feedback correction. These three steps are usually carried on continuously by computer programs on-line.
Predictive control is a control algorithm based on a predictive model of the process. The model is used to predict the future output based on the historical information of the process as well as the future input. It emphasizes the function of the model, not the structure of the model. Therefore, state equation, transfer function, and even step response or impulse response can be used as the predictive model. The predictive model has the capability of showing the future behavior of the system. Therefore, the designer can experiment with different control laws to see the resulting system output, by doing a computer simulation.
Predictive control is an algorithm of optimal control. It calculates fixture control action based on a penalty function or performance function. However, the optimization of predictive control is limited to a moving time interval and is carried on continuously on-line. The moving time interval is sometimes called a temporal window This is the key difference compared to traditional optimal control that uses a performance function to judge global optimization. This idea works well for complex systems with dynamic changes and uncertainties since there is no reason in this case to judge the optimization performance based on the full time range.
Predicative control is also an algorithm of feedback control. If there is a mismatch between the model and process, or if there is a control performance problem caused by the system uncertainties, the predictive control could compensate for the error or adjust the model parameters based on on-line identification.
Due to its essence of predictive control, the design of such a control system is very complicated and requires high level expertise although the predictive control system works well in controlling various complex process control systems. This expertise requirement appears to be the main reason why predictive control is not used as widely as it deserves to be.
5. Intelligent Control
Intelligent control is another major field in modern control technology. Although there are different definitions regarding intelligent control, it is referred to herein as a control paradigm that uses various artificial intelligence techniques, which may include the following methods: learning control, expert control, fuzzy control, and neural network control.
Learning control uses pattern recognition techniques to obtain the current status of the control loop; and then makes control decisions based on the loop status as well as the knowledge or experience stored previously. Since learning control is limited by its stored knowledge, its application has never been popular.
Expert control, based on the expert system technology, uses a knowledge base to make control decisions. The knowledge base is built by human expertise, system data acquired on-line, and inference machine designed. Since the knowledge in expert control is represented symbolically and is always in discrete format, it is suitable for solving decision making problems such as production planning, scheduling, and fault diagnosis. It is not suitable for continuous control problems.
Fuzzy control, unlike learning control and expert control, is built on mathematical foundations with fuzzy set theory. It represents knowledge or experience in good mathematical format so that process and system dynamic characteristics can be described by fuzzy sets and fuzzy relational functions. Control decisions can be generated based on the fuzzy sets and functions with rules. Although fuzzy control has great potential for solving complex control problems, its design procedure is complicated and requires a great deal of specialty. Also, fuzzy math does not belong to the Field of Mathematics since many basic mathematical operations do not exist. For instance, the inverse addition is not available in fuzzy math. Then, it is very difficult to solve a fuzzy equation, yet solving a differential equation is one of the basic practices in traditional control theory and applications. Therefore, lack of good mathematical tools is a fundamental problem for fuzzy control to overcome.
Neural network control is a control method using artificial neural networks. It has great potential since artificial neural networks are built on a firm mathematical foundation that includes versatile and well understood mathematical tools. Artificial neural networks are also used as a key element in the model-free adaptive controller of the present invention.
Generally speaking, by using most of the traditional adaptive control, robust control, predictive control, and intelligent control methods, the control system has to be designed with high level expertise to which average users do no have access. Due to the difficulty of implementing these methods, practical control of complex systems is very difficult and expensive.
A need thus exists for a general purpose advanced controller that can be used easily and effectively to control a wide variety of simple and complex systems. Such a controller should have good self learning and adaptation capabilities to cope with changes and uncertainties in the system. It should be based on the closed-loop real time input/output data and a qualitative knowledge of the system behavior only. Neither off-line identification nor precise knowledge of system dynamics should be required. In addition, the controller should not require complicated design procedures so that anyone can use it easily.